A great portion of previous research work has focused on the task of automatically inferring user characteristics through various data mining techniques See, for instance, Pennacchiotti, M., & Popescu, A. (2011), Democrats, republicans and starbucks afficionados: User classification in twitter, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 430-438; Ramage, D., Dumais, S., & Liebling, D. (2010), Characterizing Microblogs with Topic Models, Conference on Weblogs and Social Media, AAAI; and Yang, J., and Leskovec, J., Patterns of temporal variation in online media, Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, ACM (New York, N.Y., USA, 2011), 177-186.
Little attention, however, has been devoted to bringing value to the user through meaningful recommendations, such as social roles users may assume in a conversation to reach a desired goal, or conversations a user might help based on the user's expertise.
Existing or proposed social recommender systems have focused on identifying online content that could be of interest to the user, see e.g. Google Alerts; or finding other users with whom to share online content, see e.g., Amershi, S., Fogarty, J., Weld, D. S. ReGroup: Interactive Machine Learning for On Demand Group Creation in Social Networks, Proceedings CHI '12, ACM Press 2012, Bernstein, M., Marcus, A., Karger, D., Miller R., Enhancing directed content sharing, on the web, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, N.Y., USA, 2010.